RNA viruses mutate and evolve more quickly than all other human pathogens. However, RNA viruses do not all evolve at the same fast rate. It is important to distinguish the faster evolving RNA viruses because they are more likely to create drug-resistant variants or evade vaccine-induced immune responses. Whole families of RNA viruses are known to be more evolvable than others, but we know very little about what causes individual viruses to evolve more rapidly than closely related viruses in the same family. We will test whether tissue tropism could be one of the factors affecting evolutionary rates. Specifically, we are testing if genes within enteric viruses, those that infect the gastro-intestinal tract, evolve more quickly than related viruses that infect other tissues in the same hosts. Long-term nucleotide substitution rates will be calculated from members of three important RNA virus families that have large numbers of sequences in GenBank: Caliciviridae, which include the leading cause of foodborne illness in this country, Picornaviridae, which include poliovirus, and Reoviridae, which include rotavirus, the most common cause of infant diarrhea worldwide. We will contrast the rates of evolution of the same gene in primarily enteric and primarily non-enteric viruses, which will allow us an apples-to-apples comparison between viruses. The BEAST software we use takes a Bayesian phylogenetic coalescent approach to calculating evolutionary rates. The accuracy of such estimates changes over different timescales, and the time to the coalescent of some of our analyses varies over an order of magnitude (i.e., some data coalesce in 70 years, others in 700 years). Since many of the enteric virus data sets coalesce more quickly than the non-enteric virus data sets, additional controls will be conducted to ensure our results are not biased by coalescent time. Our work has the potential to open up avenues of research into whether enteric tissue tropism mechanistically leads to faster rates of RNA viral evolution, and to highlight weaknesses in a widely used software program.